The problem of learning to play various games optimally against resource-bounded adversaries is examined, with an explicit emphasis on the computational efficiency of the learning algorithm. This is aimed at providing efficient algorithms for games other than penny-matching and for adversaries other than classically studied finite automata. In particular, examined are games and adversaries for which the learning algorithm's past actions may strongly affect the adversary's future willingness to 'cooperate', i.e., to permit high payoff, and therefore require carefully planned actions on the part of the learning algorithm.
|Number of pages||10|
|Journal||Annual Symposium on Foundations of Computer Science - Proceedings|
|State||Published - 1995|
|Event||Proceedings of the 1995 IEEE 36th Annual Symposium on Foundations of Computer Science - Milwaukee, WI, USA|
Duration: 23 Oct 1995 → 25 Oct 1995